Cardiechema is a way to reflect cardiovascular disease where the doctor uses a stethoscope to help determine the heart condition with a sound map. In this paper, phonocardiogram (PCG) is used as a diagnostic signal, and a deep learning diagnostic framework is proposed. By improving the architecture and modules, a new transfer learning and boosting architecture is mainly employed. In addition, a segmentation method is designed to improve on the existing signal segmentation methods, such as R wave to R wave interval segmentation and fixed segmentation. For the evaluation, the final diagnostic architecture achieved a sustainable performance with a public PCG database.

The purpose of study is aimed to solve the problem associated with autonomous driving on the natural paths of planets. The contribution of this work is to propose an improved deep deterministic policy gradient (DDPG) framework for the autonomous driving on natural roads requires handling uneven surface of different throttle and braking reaction speeds. Our new finding is to design an adapted DDPG algorithm by double critic and excellent experience replay as DCEER- DDPG to reduce the overestimation of state action values. In addition, we created a virtual reality environment with TORCS simulator for fair evaluation. In the experiments, the proposed DCEER-DDPG has better performance than previous algorithms, which can improve the utilization of driving experience on a natural path and increase the learning efficiency of the strategy. For the future applications, the proposed DCEER-DDPG is used not only on Earth, but also in lunar exploration.

Background: With recent technology, multivariate time-series electrocardiogram (ECG) analysis has played an important role in diagnosing cardiovascular diseases. However, discovering the association of wide range aging disease and chronic habit with ECG analysis still has room to be explored. This article mainly analyzes the possible relationship between common aging diseases or chorionic habits of medical record and ECG, such as diabetes, obesity, and hypertension, or the habit of smoking. Methods: In the research, we first conducted different ECG features, such as those of reduced binary pattern, waveform, and wavelet and then performed a k-means clustering analysis on the correlation between ECGs and the aforementioned diseases and habits, from which it is expected to find a firm association between them and the best characteristics that can be used for future research. Results: In summary, we discovered a weak and strong evidence between ECG and medical records. For strong evidence, most patients with diabetes are always assigned into a specified group no matter the number of classes in the k-means clustering, which means we can find their association between them. For weak evidence, smokers, obesity, and hypertension have less unique ECG feature vector, enabling clustering them into specific groups, so the ECGs might be used to identify smokers, obesity, and hypertension. It is also interesting that we found obesity and hypertension, which are thought to be related to cardiovascular system. However, they are not highly correlated in our clustering analysis, which might indirectly tell us that the impact of obesity and hypertension to our body is various. In addition, the clustering effect of waveform feature is better than the other two methods.

In recent years, due to the popularity of wearable and implantable devices, the application of electrocardiogram (ECG) recognition for human identification has become more and more feasible. ECG is an important biological signal, which can be used for the diagnosis of cardiovascular diseases. After more than 20 years of research, it has been established that the ECG signal is unique to individual. This article intends to summarize the previous algorithms and applications in ECG recognition for human identification, while introducing the concept, history, algorithm summary, hardware platform etc. In addition, we also discuss some important issues in order to provide a helpful guideline for future research.